This project will visualize internal and surface 3D- tongue motion during speech based on ultrasound and tagged Cine- MRI data. The project will use kinematic, dynamic and geometric models to capture, manipulate, and reduce the dimensionality of the major patterns underlying tongue behavior during normal speech production. These normative data can then be compared with patient populations. Three projects will be executed. The first will reconstruct the movement of 3D tongue surfaces and volumes during speech. We have already determined a minimal, optimal set of slices for reconstructing 3D surface motion from 2D ultrasound slices. The proposed experiments will add to this work by reconstructing 3D motion of internal tissue points from tagged-MRI. The observed internal motion will allow us, for the first time, to complement EMG by inferring tongue muscle activity from MRI. The value of such data is twofold. First, visualization of a complex speech movement patterns allow us to identify and quantify underlying patterns of movement and shape. Second, the data are useful clinically for comparing normal and disordered speech patterns, and estimating the underlying musculature. The second project, which is related to the first, will classify tongue motion based on biologically important tongue surface features. Geometric shape modeling will be done on 3D tongue surface shapes and motions. Geometric models extract global and local motion features that reduce the dimensionality of 3D structures, and identify and classify underlying patterns. This will be particularly useful for determining group and individual subject behavior patterns, and removing noise. In order to test hypotheses of muscle-to-surface linkages, the third project will develop a family of 3D mechanical tongue models. We have already developed 2D (one slice over time) kinematic and dynamic models of tongue motion. Our kinematic models represent surface and internal deformation of the tongue and reflect its underlying physiology. These models can fill gaps in the reconstructed 3D motions (Aim 1) and will provide input and insight into the dynamic predictive models. Dynamic finite element models will link internal muscular activity to surface shape motion. The 3D models will be physiologically accurate and can be made more or less complex to suit the experimental condition.